The aim of our work, reported in this paper, is to build up a dataset for evaluation of the effectiveness of automated approaches to document expansion with images.

the problem that we address in this paper is that of augmenting the text of children's short stories (e.g.


For the queries, we used popular children's fairy tales since most of them are available in the public domain and are freely distributable.

we make use of 22 short stories collected from "Aesop's Fables" 5 .


In this paper, we described the construction of a dataset for the purpose of evaluating automated approaches for document augmentation with images.

we addressed the problem of automatically illustrating children stories.


Instead, it is based on ENB issues covering scientific report creation discussions by the Intergovernmental Panel on Climate Change.

the ENB-IPCC test-set does not come from the same corpus that the system was developed for, and serves as a way to test the system on texts from a slightly different domain, in terms of syntactic structures and lexical items involved.


The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English.

we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication.


In this paper we present the datasets created by CLEF eHealth Lab from 2013-2015 for evaluation of search solutions to support common people finding health information online.

the CLEF eHealth information retrieval (IR) task of this Lab has provided the research community with benchmarks for evaluating consumer-centered health information retrieval, thus fostering research and development aimed to address this challenging problem.


The second CLEFeHealth (Kelly et al., 2014) expanded our year-one efforts and again organized three tasks.

the first task aimed to help patients (or their nextof-kin) by addressing visualisation and readability issues related to their hospital discharge documents and related information search on the Internet (Suominen et al., 2014).


Next, multivariate statistical techniques such as multidimensional scaling (MDS) and cluster analysis can be applied to the table of prosodic distance.

using the cluster analysis, the researcher could detect the existence of clusters of locations (speakers or another variable of interest) according to their closeness in terms of the measure of the prosodic distance.


The main contributions of this paper are introduction of the retyping interface approach, and an analysis of how retyping and the described three traditional iterative and non-iterative interfaces compare in terms of transcription quality and speed, in which we analyze how segment difficulty, mechanical effort, and other factors influence quality and speed.

we carry out a user study in which expert and non-expert participants transcribe automatically selected segments based on an ASR transcription, using the  Figure 2: The four interface combinations: Typing fromscratch in an empty box (top), post-editing the ASR output (bottom), and unrolling the ASR output in sync with the audio while coloring low-confidence words in red (right).


We tested the significance of all fixed effects in our models via likelihood ratio test (for p ≤ 0.05).

we built a null-model with the effect in question removed, and examined whether this significantly reduced the model likelihood.


The relative distances between the methods remain the same.

humans appear better at choosing acoustically similar words than the ASR, even if the word is incorrect.


The fitted model is displayed in Figure 9 and reveals that from-scratch editing is faster than post-editing for high PE typing efforts above 67%.

for comparable mechanical typing effort from-scratch is faster, confirming our hypothesis about post-editing overhead.


In order to account for phonemic effects such coarticulation the monophones were expanded using the full linguistic context.

for each phoneme in an utterance we take into account its left and right neighbors, stress information, position in a syllable, distinctive features and so on, resulting in 271 distinct contexts.


In this paper, we describe the Speech Trax system that aims at analyzing the audio content of TV and radio documents.

we focus on the speaker tracking task that is very valuable for indexing purposes.


Finally, an approach relying on TV material issued form the REPERE challenge (Giraudel et al., 2012) is proposed in (Fredouille and Charlet, 2014).

the authors investigate the i-vector framework and propose a specific protocol to identify candidates using a 533 speakers dictionary.


In this section, we focus on the speaker recognition feature of our demonstrator.

we describe how our speaker dictionary is created and detail the implementation choices made for the speaker recognition system itself.


Finally, following the famous zoo introduced in (Doddington et al., 1998), one can notice that several speakers appear to be wolves, meaning that they are particularly successful at imitating other speakers.

their speech is very likely to be accepted as that of another speaker.


Even if phone remains the predominant contact channel in companies' interactions with their customers, interaction modes are much more diverse nowadays and Customer Relationship Management has to integrate multi-canality in their analytics measurements.

chat conversations are growing very fast and are appreciated by both customers and agents who consider it as a way to remain in direct contact with customers while preserving a certain distance and avoiding disrespectful exchanges.


We use the MediaWiki API to obtain information present in each editor's Wikipedia user account.

2 we extract the gender, registration date, and aggregate edit count of each editor.


It shows that about 80% of the posts in our corpus is authored by 20% of the editors.

our corpus represents a considerably large number of interactions between the same set of people.


In this case, most of the drop affects the recall (10%), while precision decreases by a 1.9% only.

referent detection is more sensible to parser errors than the detection of antecedents.


We have opted for annotating already existing datasets with anaphoric information to enable studies involving several discourse-level phenomena.

our news texts come from the Wall Street Journal dataset used in the RST Discourse Treebank (Carlson et al., 2002).


The original annotation guidelines have been created and tested for the first release of ARRAU (Poesio and Artstein, 2008).

the coders were requested to annotate each nominal markable, mark its referential status and basic morphological properties and then provide coreference and bridging links between (a) two markables or (b) a markable and a discourse segment.


In ARRAU, we focus on different types of noun phrases.

we label markables that do not participate in coreference chains: singletons and non-referentials.


Observed algorithm accuracies leave some room for improvement.

the macro-kappa of 0.6 that we measured in our functional analysis experiments bears further study.


Although we view a reliance on more supervision as an impediment to broad-scale use, there are use cases in which the labor investment might be warranted.

we treated every structured region as being independent of all others, when in reality structuring conventions are often repeated within documents and across collections.


In general, the base-line, that reaches an accuracy of 68.5%, is largely outperformed by the two models.

the base-line classifies events following this simple algorithm: • if the event is in a future tense, then the factuality value is IND • else, if the event is preceeded in the sentence by some negative word, then the factuality value is NR • else, the factuality value is R for IND events the base-line reaches just a 33.6% of F-Measure.


The objective of this model is to predict: where w j are words, represented as one-hot vectors in the model.

the model tries to determine the probability of the occurrence of a word given a context window.


Looking at the most promising features in the large dataset we considered in this study, it seems that speech features are generally more reliable in distinguishing controls from MCI subjects.

spectral Centroid mean (sPE_sPCENTRM) and the statistics about speech and silence duration intervals are consistently present as significant features in all tasks.


The speech of ID people usually presents multiple disorders affecting the different components of language (syntax, semantics, phonology and pragmatics) (Martin et al., 2009) (Chapman, 1997).

prosody is also affected (Stojanovik, 2011) conditioning their communicative potential.


the order of the color names was randomized and 11 color associations were obtained (see also Figure 2).


We go beyond previous work on extracting verb relations from corpora and propose a novel semantic verb relation scheme designed for the challenging task of automatic information consolidation in the particular domain of everyday educational topics.

we make the following contributions: • We developed a linguistically motivated hierarchical annotation scheme of fine-grained semantic verb relations between propositions (Section 3.


(2008) investigated the applicability of crowdsourcing to five semantic NLP tasks: word similarity, event annotation, word sense disambiguation, sentiment analysis and textual entailment; they concluded that crowdsourcing was well-suitable for these tasks.

crowdsourcing of paraphrase and textual entailment annotations came into focus of multiple recent studies.


complementarity, antonymy, converseness and reversiveness) are actually perceived by the crowd.

we collect data for Italian by using the crowdsourcing platform CrowdFlower.


antipodals, complementaries, disjoint, gradable opposites, reversibles).

observing the collected data the authors find that annotators agree markedly on identifying contrasting word pairs while there is a variation in the agreement in the questions that aim at identifying the different kinds of opposites.


In the third step (type-of-opposition identification), we aim at determining which type of opposition the two verb senses hold.

for the opposition identification step, we show annotators a pair of sentences, S1 and S2: S1 contains the source verb and S2 is identical to S1, with the exception of the source verb, which is substituted with the target verb in S2.


In order to acquire information on the type of oppositions, we focused on the types of relation described in Section 2: complementarity, antonymy, converseness and reversiveness.

we based our experiment on the characteristics of these relations as identified in the literature-Section 2.


We ask annotators to evaluate which of these statements can be correctly apply to the pairs of verbs they are asked to consider each time.

: statement 2 characterizes complementarity, statements 3 and 4 antonymy, statements 5, 6, 7 reversiveness and statements 8 converseness.


As previously described, we ask annotators to provide judgments on the types of opposition existing between a source verb and a target verb by considering two sentences S1 and S2.

s1 is a sentence that contains the source verb and is extracted from the annotated corpus of the T-PAs resource, while s2 is created by automatically substituting the source verb with an opposite target verb (see Table 2 for an example).


In previous work (Gkatzia et al., 2015) we have used the REAL corpus to automatically predict the success of REs 1 .

the corpus is annotated with the following measures of success: Reference to the type of object that is aimed to be described.


Then, a 2 × 3 analysis of variance (ANOVA) was performed on the factors of communication environment (Chat or FaceToFace) and chat type (VD, VD or TX) for each emotional dimension.

multiple comparison tests were performed with the Tukey-Kramer method to investigate interactions across the factors.


In that case, to achieve a natural and human-like interaction, it is critical that computers be capable to reciprocate in these affective patterns.

in Japan, the need of an emotion sensitive system continues to escalate.


for anger, valence ranges from -1 to -3, and activation ranges from 1 to 3, and so on.

the emotion  Table 3: Emotion label sets dimension labels serve as a more fine-grained information of the emotion class labels.


For each word-emotion pair w,E , we calculated a syntagmatic emotive score (SintScore) based on the association measure (AM) such as the PPMI between w and the seeds of E : SintParModel: In this model, we restricted the contexts to the words with a sufficiently high cosine similarity with the emotive centroid vectors and a sufficiently high syntagmatic emotive score.

we selected the contexts having, at least for one emotion: where and the association measure is the PPMI.


Top1000EmoPos: In this second model we followed the algorithm proposed in Zhitomirsky-Geffet and Dagan (2009) to bootstrap the emotive contexts starting from a standard approximation of the similarity space.

we adapted their "Bootstrapped Feature Weight" (BFW) to capture both syntagmatic and paradigmatic properties of the words.


To compute Precision, Recall and F1, given a emotion, word-PoS pair, we used a fixed threshold on the number of CF raters and a variable threshold on the cosine similarity (Table 4).

we sorted the candidates according to their cosine similarity for each emotion, PoS pair, and the threshold was fixed at the 3 rd quartile for each group.


These effects could be due to the fact that in our feature elicitation experiment ANGER terms tend also to be associated with DISGUST and many JOY terms are also associated with TRUST and SURPRISE.

the distinctiveness of the seeds impacts on the final results, but the effect is more evident if we consider a random sample of words which includes emotive words with a moderate intensity.


In this paper, we consider the task of voice-based postediting of the output of a machine translation (MT) system.

a human corrects the translation produced by a system simply by speaking out this correction.


When varying the quality of the ASR systems, we found that the CONFIDENCE and CONFIDENCENODUPLICATES heuristics have similar scores, and outperform the ASR only when its accuracy is considerably low; however, in that case, the use of MT alone offers a better option.

a priori knowledge of the accuracies of ASR and of MT leads to simply selecting the output of one or the other, depending on their accuracy, with no need for a combination method.


The first scenario has the following drawback: when we perform forced decoding using an phrase table that is not exactly the same as that of the original system, the given reference translation is likely to be unreachable.

the system can lack phrase pairs that translate source phrases to the given reference phrases.


Annotations created with BRAT are stored in a stand-off 2 format.

annotations are stored into separate text file, with the original data remaining unchanged.


In future development of this method, it could therefore be helpful to add a part-of-speech constraint.

the two perception lexemes must be in a dependency relation and they must pertain to different part of speech.


It is assumed, of course, that tokens matched by NAP (and NAP _GEN ) must agree in number, case and gender.

the program first extracts the longest (maximal) phrases consisting of a noun phrase, possibly modified by other noun phrases in the genitive case.


conjunctions cannot introduce single clauses, they can only link two clauses.


One argument against this interpretation is the fact that it can appear in an isolated clause (example 5).

it does not respect the passive valency definition for CONJ described in Section 2..


The traditional definition of idiomaticity is based on semantic non-compositionality.

the meaning of the parts does not add up to the meaning of the whole.


The increasing availability of psycholinguistic, lexical and ontological resources and of more precise and robust natural language processing tools has enabled the automatic annotation of language acquisition corpora with additional sources of information.

among them resources like WordNet (Miller, 1995) and specialised datasets contain information about Multiword Expressions (MWEs) such as noun compounds (police car) (Kim and Baldwin, 2006;Nakov, 2008a), phrasal verbs (break down) (McCarthy et al., 2003) and collocations (e.g.


Nesselhauf (2005) and Farghal and Obiedat (1995) report that learners of English paraphrase their discourse in order to avoid using collocations that they do not master.

a learner knows the meaning they want to express, but they fail to do it by means of a collocation, which means that they fail to pick the collocate that expresses this meaning.


The first stage (lines 4 − 9) consists, first, in retrieving a candidate set by means of the function relSim, which computes the similarity of the relation between b φ ι and ς φ ι to the relation between b ι and a hidden word x. relSim can be thus interpreted as satisfying the well-known analogy "a is to b what c is to x", exploiting the vector space representation 4 of a, b, and c to discover x (Zhila et al., 2013).

we compute v b −v a +v c in order to obtain the set of vectors closest to v x by cosine distance.


Although the existing parses and dumps already satisfy numerous specific research needs, they have certain limitations that drove us to build our own resource: the Syntactically Annotated English Wikipedia Dump (SAEWD).

we address the following shortcomings: the lack of constituents-based tree information, the complex output formats, the limited online access and the absence of the tools used (i.e., the source code) to create the annotated corpus.


For each word, we store its complete path in the constituency tree.

we keep all the nodes of a word's own branch from the root to the word itself.


Brigand's parent node is a Noun Phrase (NP) node which in turn comes from the root of the tree, the Sentence node S. We assign to each phrase chunk an identifier (22 and 97 in this case) in order to distinguish them according to their building elements as specified by the grammar rule used.

a phrase chunk, e.g., a NP, a Verbal Phrase (VP), a Prepositional Phrase (PP), or other chunk defined by the grammar in CoreNLP, may be built from different types of PoS tags.


Finally, approaches based on predictive modeling use classifiers to spot trees or nodes that are likely errors.

ule and Simov (2004) go beyond single CFG expansion and look at the correlation be-tween grandparents and daughter lists; Volokh and Neumann (2011) use an ensemble of two off-the-shelf parsers that generate a re-annotation of the training set.


The fundamental idea underlying this project is to discover the common and universal categories across languages as well as the language-specific ones.

while the annotation scheme in UD stipulates a consistent annotation of similar constructions across languages, it also allows language-specific extensions or omissions when necessary.


Fixed expressions in attached forms and different types of unsegmented clitics such as pronominal and copula clitics are not separated from their head word.

these cases have not been treated at morphological level, 2 The treebank is open source and freely available in CoNLL-format and can be downloaded from http://stp.lingfil.uu.se/∼mojgan/UPDT.html 3 The treebank data is extracted from the open source, validated Uppsala Persian Corpus (UPC), which is currently the largest freely available corpus of Persian.


de/dictionaryjournal/) provides a typical use case for our Concepticon: The project will publish dictionaries of lesser-described languages in a way that maximizes opportunities for data reuse.

comparability across dictionaries via comparison meanings ("standardized" meanings that ease the comparison) is a goal.


However, while some can almost immediately be converted to LLOD, for others there is no straightforward way to achieve this.

compared to modern conceptual frameworks used in LLOD representation, the LVF conceptual system is not completely specified and made explicit.


We evaluated the impact of expanding sar-graphs with FrameNet phrases with a relation extraction experiment.

we were interested in whether the addition would substantially increase the coverage of linguistic expressions.


Because of the different granularity and level of completeness of the two resources the baseline contained a large number of entirely unsatisfactory mappings of the types (iib) and (iiib).

there were many verb frames that could be found only in one of the resources, in spite of the fact that the verb itself was present in both of them.


Since its first conceptualization in 2011 and its first materialization in 2012, considerable work has gone into improving the definition and infrastructure that supports the cloud, with the result that, since Spring 2015, the cloud is generated on a monthly basis, as shown in Figure 1.

we have further refined the criteria for inclusion and the methods for tracking resources.


An important criterion is that a dataset must be linguistically relevant in that it provides or describes language data that can be used for the purpose of linguistic research or natural language processing.

we define the following kinds of resources: 1.


In order to bring resources into the linked data cloud we rely on the metadata recorded in Datahub.

we attempt to find resources by looking for specific groups and tags that are associated with linguistic resources.


As for the design of the technical requirements and format characteristics of the corpus data and annotations the results of the technical part of user study as well as general design principles from usability standards (ISO 9241-11) were taken into consideration.

target group orientation has been implemented in the macro-as well as in the micro-structure of the platform in the following ways: • Implementation of different search areas, which respond to specific needs of the different target groups regarding search as well as results display • Modelled usage scenarios for different user groups • Implementation of different help and support structures Usability standards are respected by providing for selfdescriptiveness, controllability of the interaction and error tolerance.


The search interface (3) combines four different areas, which respond to specific needs of the different target groups regarding search as well as results display.

the user study indicated that language teachers, testers and teacher trainers have similar demands that focus on the grouping and retrieval of texts and learner language features, while linguists demand finer-grained linguistic search options.


Furthermore, comments revealed that more sample searches and a clear guidance on the differences and connectivity between the four search areas would be helpful.

the users had difficulty to understand that the 'document search' serves to create subcorpora for use within simple and advanced search modes.


In this contribution I will sketch the major characteristics of CLARIAH and the rationale behind them (section 4).

i will describe the rationale behind joining forces for work on CLARiN and DARiAH in the Netherlands.


As a result, SYN2015 is designed as representative, but not claimed to be balanced.

1 SYN2015 is designed as a representation of contemporary printed language of the last five-year period, i.e.


SYN2015 can be searched via KonText 2 interface which enables users also to examine corpus composition and to make use of the wide variety of included texts intuitively and effectively.

it is possible to create subcorpora according to any selected combination of the 2 Corpus query interface developed at the CNC as a fork of the NoSketch Engine and based on Manatee as the backend (Rychlý, 2007;Machálek & Křen, 2013); KonText is available at http://kontext.korpus.cz/.


Such initiatives are for instance the META-SHARE platform (Gavrilidou et al., 2012), the CLARIN VLO (Broeder et al., 2010) and the LRE Map (Calzolari et al., 2012;Del Gratta et al., 2014b;Del Gratta et al., 2014a), with their sets of metadata.

the LREMap was launched as an initiative at LREC 2010 in order to crowdsource reliable and accurate documentation for the largest possible set of resources.


The selection of a particular e-mail was not biased at all (although we assume that thematic considerations and private concerns played a decisive role for the final choice) and occurred at the free will of each donor.

we did not articulate any content or formal preferences.


The For example, for Turkish the important properties of the preceding morpheme are the frontedness and roundedness of the final vowel, whether the ending consonant is voiceless, and so on.

we choose to specify a list of categories for the last vowel, which can be one of FR/FU/BR/BU, where F/B stands for front/back and R/U for round/unround.


The reason for this is that they work together with the stem names as placeholders in the concrete abstract specification, as discussed in Section 3.2.1.

the line means "a noun with morphological properties V:FR C:VL followed by the plural morpheme lEr", and the line means "a noun with morphological properties V:FU C:VL followed by the plural morpheme lEr".


5 In some Russian loanwords in Tuvan, however, affix vowels harmonise as front and unrounded, despite the previous vowel being back and sometimes rounded.

harmonising affixes immediately following words ending in ‹бль›, such as ансамбль 'ensemble' and рубль 'rouble', are always front and unrounded.


The mentioned works were published as paper books, but they were based on data prepared using databases.

the verbal database became the seed for description of other parts of speech.


Repertoire of grammatical categories and their values is based on the tradition of Polish grammar, but it also uses solutions proposed earlier by the members of the group (Saloni et al., 2007a).

the dictionary uses the detailed system of 9 genders proposed by (Saloni, 1976) and some non-traditional grammatical categories including accomodability for numeral forms (Saloni, 1977) and depreciativity for masculine personal nouns (Bień and Saloni, 1982;Saloni, 1988).


When designing the application we strove to provide a user experience close to that of a desktop application.

to display the list of entries (left part of the screen in Figure 1) we use the SlickGrid JavaScript component that allows to create the illusion that the full list of entries is displayed directly in the browser.


Morfeusz and its SGJP-based dictionary are distributed under the liberal two clause BSD license.

the clear text form of the list of all inflected words with Morfeusz tags is made available.


Since the IT-TB and the LDT are not balanced to be representative of Latin (or of a variety of it), we enhanced the corpus-driven building of LV with a number of intuition-based entries.

we wanted LV to include the lexical entries for all the valency-capable words occurring among the first 1,000 most frequent words of Latin reported by Delatte et alii (1981).


Figure 4 shows one of the outputs of the query pictured in figure 3.

it reports one frame entry for the verb confero ("to confer").


Given the current situation where a variety of language resources have been actively developed and disseminated, integrating these independently developed resources, preferably in a standardized format, has become an important issue (Cimiano et al., 2013).

aligning lexicalsemantic resources (henceforth, LSR) across languages in the word-sense/lexical-concept level is highly demanded for realizing cross-language semantic applications.


Although the filtering and the classification could be simultaneously performed by a single classifier, we propose that a two-tiered classifiers configuration could be more adequate.

the first classifier filters out presumably irrelevant correspondences, and then the second classifier assigns a relatively fine-grained relation label to each of the survived correspondence candidates.


This exactly motivates the presented work.

we would like to filter out irrelevant correspondences, and assign a label that signals the type of semantic relation to each of the relevant correspondences.


We adopt a supervised learning approach.

we train a classifier, given hand-annotated training data.


Features for representing a correspondence candidate can be divided into two feature groups: (1) features for a source/target concept node, and (2) features for a source- 3 Our annotators were largely puzzled with the initial inventory.

the distinction between hyponymy with meronymy was not only hard for them but sometimes impossible to do, suggesting that the task of cross-lingual classification of semantic/conceptual relations for independently developed LSRs involves innate difficulties.


In summary, the results presented so far would justify our proposal on the system-architectural issue.

the two-tiered classifiers configuration could be more feasible for classifying cross-lingual correspondence candidates be-  tween different LSRs.


In the case of Czech, a large parallel corpus InterCorp (Čermák and Rosen, 2012) (version 8) was used to automatically extract a Czech--English bilingual dictionary.

we used its manually sentence-aligned fiction core data, as well as journalistic packages from PressEurop/VoxEurop and Project Syndicate.


the 4lang dictionary itself was shown in (Kornai et al., 2015).

defini- 3 The 20 words in our sample, selected randomly using GNU shuf were the following: aircraft, characteristic, clothesline, contrived, cypress, dandy, efface, frustrate, incandescent, khaki, kohl, lizard, nightie, preceding, residency, rock-solid, scant, transference, whatsit, Zen tions in the 4lang dictionary can be stated using no more than 129 primitives.


The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants.

it includes a comprehensive and powerful phraseological component.


It is possible for multiple LUs to correspond to the same frame.

this concerns reflexive and non-reflexive verbs, provided that they represent the same meaning (diathesis alternations, e.g., ZBIĆ and ZBIĆ SIĘ 'break').


Therefore, we mainly modelled on VerbNet.

apart from roles we use selectional preferences, just as in VerbNet.


While its syntactic layer is nearly finished, the work on the semantic layer is in progress, only about 6 300 (52%) entries are semantically elaborated.

integrating the phraseology in Walenty and PLWORDNET should be developed.


Differently from most of the researches above, our aim has not been to directly translate VerbNet in Italian, but rather to develop a new, autonomous and independent classification for Italian verbs, accounting for Italian the syntactic peculiarities, and stemming from the integration of various resources and theoretical frameworks, and yet compatible with Levin/VerbNet classes (henceforth: L/V) The method that we propose to build an Italian verb classification is rooted in the Construction Grammar paradigm (Goldberg 2006, Hoffmann andTrousdale 2013).Moreover, we adopted a radical distributional 2 approach.

the classes were found by examining corpus-based data and empirical distributions.


The greater or lesser degree of prototypicality is given by the number of typical constructions of the class the verbs participate in: each class contains a group of "core" verbs that share all the syntactic and semantic characteristics of the general class, and fuzzy boundaries of less prototypical members.

we argue that specific semantic properties of a specific verb meaning may "block" the realization of some constructions that are instead shared by most of the other verbs of a class, or instead add some idiosyncratic constructions as well.


Seven Italian verb classes were constructed.

these classes include verbs of KILLING, PUTTING, FILLING, REMOVING, CLEARING, SENDING AND CARRYING and of CHANGE OF POSSESSION (see appendix).


: subclasses based on the presence vs. absence of a syntactic construction that is not possible in Italian, like the Double Object construction), or when other solutions were regarded to better describe the verb behavior.

during our analysis we established several mismatches between the English and the Italian models, due to syntactic or semantic differences between the two languages, or discrepancies deriving from the construction methodologies.


On the one hand VENDERE verbs are listed under the DARE subclass and take the perspective of the Seller, which gives Goods to a Buyer in exchange for a sum of money.

they lexicalize the receiver of the action (the Buyer) as the complement of a PP headed by {a}.


The idea was to show feasibility of linking two resources, rather than finding an optimal granularity for such alignment.

we used surface forms of ukWaC and WaCkypedia EN corpora by Baroni et al.


We evaluate our linking techniques with respect to a manual mapping of senses.

we built an evaluation dataset for 50 ambiguous words presented in Table 2.


Lexico-semantic resources such as WordNet (Miller, 1995) play an important role in many natural language processing (NLP) tasks.

for applications that involve processing of events and states, resources that model verb semantics are of great importance.


Other than this future work on this specific enlargement experiment, we are working on algorithms to validate variants and synsets already existing in PULO.

in the next tasks we will prioritize the task of validating what we already have, changing variants confidence measures and, possibly, removing some existing mistakes.


All combined features significantly outperforms their component features, indicating each feature is complementary for one another.

the reason that combining CV with WV outperforms WV is due to WV's smaller coverage (using 0 vectors for unseen words), which indicates that using CV might ease the pain of encountering new words.


As mentioned before, sentiment shifters can reverse the polarity of the given text, and hence are vital for polarity classification.

in the drug domain, most of medical terms such as "pain" and "depression" are negative, but they occur frequently in positive sentences.


However, data from our corpus rule out DLTAG, LDM, and RST as candidate theories because they posit tree-based discourse structures.

our corpus contains frequent acknowledgments or other moves directed at more than one player as shown in this excerpt from our corpus:  (235,236,238).


Coherence of an utterance assesses whether a system response is acceptable given the directly previous human utterance.

an exchange of two utterances is said to be coherent if the presence of the second utterance is easily explained by taking into account the first one.


For the entire corpus, the ratio is on average 0.92 (median=0.93; std=0.08; min=0.73; max=1.0).

92% of the utterances of the system are unique in a dialogue on average.


We did not find significative link between features of dialogues and the reported enjoyability.

we did not observe significative links between enjoyability and the capabilities of the system in terms of politeness, understandability, coherence and relevance (unlike what was found for amusement and engagement).


Discussion: Lessons from a Data Collection with a Chatterbot system response.

we envision a more robust automatic filtering process to limit the number of incomprehensible system utterances.


Next, our system has unsurprisingly shown clear limitations on the coherence and relevance aspects that should be improved in the near future.

our results show a direct link between the reported system response coherence, and the amusement and engagement of the human in the interaction.


First, it has confirmed that the engagement of interaction reported by the human participants in chatterbot usage is dependent on other factors than the system response selection process.

participants have been complaining in the free form comments about the lack of initiative of the system (which is only reactive).


Since the occurrences of BCs are dependent on the part-of-speech (P OS) occurring just prior to the BCs and listener's character, the models were defined as probabilities yielded by the immediately preceding parts-of-speech and the listener.

bCs (o) of type j by person (i) are represented by p ij = P (o j |P OS, i).


Kendall rank correlation coefficient (τ ) is used to measure the correlation between metrics' scores and human ranking.

we use the definition of Kendall τ presented in Macháček and Bojar (2015) which was the official measure for the WMT15 Metrics Task.


It is expected to convey correct information between two people who do not understand each other's language, sometimes in critical situations.

the measurement of the quality of MT systems should include not only the intrinsic metrics such as accuracy and fluency but also how often a user can complete his/her purpose in using it.


In our previous paper, we proposed a light weight, humanin-the-loop extrinsic evaluation scheme (Matsuzaki et al., 2015), where MT systems are evaluated by human subjects' scores on the second language ability tests translated to the subjects' native language by MT.

we used dialogue completion questions (Figure 1) as the test material.


The top fifty terms in each list show the heavy presence of lexical items related either to the semantics or to the unique syntax of the domain.

we see heavy representation of words related to diabetes, body parts, and symptoms.


We declare an element Y of an accepted frame X optional if X \ Y is also an accepted frame.

if the empty frame is accepted (this may happen to some words outside the initial I cluster), any single argument will be by definition optional.


We address the task of automatically correcting preposition errors in learners' Dutch by modelling preposition usage in native language.

we build two models exploiting a large corpus of Dutch.


140,000 words from the Eindhoven corpus (newspaper text), about 15% of all tokens are prepositions, despite only representing 0.66% of all types (n=15,863), with the top 15 prepositions comprising almost 90% of all preposition use (see Table 1 for details).

prepositions are used very frequently despite being a relatively small lexical class.


This might be due to the fact that negative preposition vectors are all extracted in the same way (as explained in Section 4.1.).

it could depend also on the way the data is represented.


Another interesting possibility to be investigated in this context would be to add artificially generated errors to the native training corpora (as suggested in (Rozovskaya and Roth, 2010)), so as to reduce the differences between native and learner language.

rozovskaya and roth (2010) show that introducing artificial errors in article usage in English in the training data increases results on learner test data.


In this paper, we focus on one particular discontinuous verb phrase construction.

we look at the verbparticle (V-Prt) split in English and German, such as in the examples below: (1) take V the shoes off P rt (2) macht V schon wieder blau P rt From a syntactic perspective, these two languages behave very differently: in German, the particle position is clausefinal and a particle can be separated from its verb by an embedded clause, while in English a particle can be separated from the verb only by its direct object and a very long split is impossible.


NULL alignments such as those illustrated in (7) are potentially detrimental for MT quality since they add weight to incorrect translations.

they affect noncompositional V-Prt phrases.


Our method of implementing this idea differs from what Gross did for French (through syntactic tables).

we introduce the so called semantic role relations such as Agent, Object, Patient, Beneficient 8 between verb and noun synsets to encode how the verbs and nouns combine to form simple sentences.


In Figure 6 (a), (b), we show the correlation between the valence estimated and human collected ratings in English and Greek, respectively.

both context and co-occurrence based semantic similarity metrics were tested with the semantic-affective model.


Nevertheless, many of the sentiment expressions are context-dependent in the opinionated texts (Ahn et al., 2012).

a given polarity word may indicate different opinions depending on its domain, or even within the very same domain, depending on the given entity or aspect that the polarity expression is connected to (Ahn et al., 2012).


For instance in The minister does not force the president to cheat nothing happens at all, neither minister nor president receive an effect.

the main clause (predicate) is non-factual as well as the subclause (predicate).


From the total of 1,023 single verb roles (i.e., the sum of the number of roles of every verb frame that we annotated) 495 are effect roles.

528 roles are not affected, they are neutral, so to speak.


It includes 4,129 posts published from September 15th, 2014, to November 15th, 2014, on the consultation website.

we collected 2,043 posts from focus groups ("dibattiti diffusi" in Italian) composed of student, teachers, schools, citizens, who joined the consultation about the laBuonaScuola reform in order to express their opinion on the subject.


It is interesting to notice that all users posting the most influential tweets, according to this measure, are related to the government which is proposing the bill.

two of the three messages have been posted from the Prime Minister Matteo Renzi, whereas the third one has been published from the official account of the Ministry of Education.


Future work includes the expansion of the lexicon, and experimenting with other types of datasets.

we are currently working on assigning sentiment scores to the entries in the lexicon.


The first pass is performed by junior annotators, followed by a pass of quality control by senior annotators.

the alignment task includes: 1) Link morphemes in the source (Uzbek and Turkish) language to those in the target language.


We defined a set of heuristics and statistics that would help us find matches quite accurately.

we looked at the following three characteristics to find likely matches: • Publication date.


If we zoom in on Figures 1 and 2, we see that for most TV programmes, the different episodes of one programme have similar TV ratings in general.

the number of watchers for a programme are constant during the season.


We investigate the effects of sampling on Twitter flu detection by randomly sampling from our Twitter corpus and computing correlations with Google Flu Trends using the sampled data.

we randomly select n% of the tweets from the full Twitter feed, use our methodology to estimate the ILI prevalence based on these tweets, and compute the correlation between our prevalence estimate and Google Flu Trends' estimate.


To determine whether monthly aggregation is changing the rate at which prevalence estimates converage, for each sampling level we compute how close an estimates' correlation with GFT is to the correlation's maximum value.

we compute corr s /corr 1.0 where corr s is the correlation with GFT at sampling level s. The results over all 70 cities are shown in Figure 6.


In this section we investigate how well this performance increase correlates with the total number of ILI tweets used in the analysis.

we investigate whether poor preformance at low sampling levels is caused by a low number of tweets.


To correct for cities that always perform poorly, we can measure each city's fraction of its maximum GFT correlation at the 1% sampling level rather than measuring its GFT correlation at the 1% sampling level directly.

we compare the tweet count to corr 0.01 /corr 1.0 , where corr s is the Pearson correlation between Twitter and GFT at the s sampling level.


According to Landis and Koch (1977) interpretation scale, these results are acceptable, lying between moderate (CMR) and strong (TST).

it is interesting to note that qualitatively there is no difference between those who are familiar with the annotation task (our NLP and InfoCom experts) and those unfamiliar with the task (CMs) as the inter-annotator agreements obtained on the two corpora are almost the same.


In contrast, word clustering groups together different but semantically similar words that pertain to a specific sense (e.g., money, loan, and finance for the financial sense of bank).

word clustering based on co-occurrence graphs posits that senses of a polysemous word correspond to strongly interconnected components of its co-occurring words.


Our overall goal is to enrich two parallel corpora, Europarl (Koehn, 2005) and the QTLeap corpus (Agirre et al., 2015b), with token, lemma, part-of-speech (POS), namedentity recognition and classification (NERC), named-entity disambiguation (NED), word-sense disambiguation (WSD) and coreference for six languages covered in the QTLeap project, namely, Basque (EU), Bulgarian (BG), Czech (CS), English (EN), Portuguese (PT) and Spanish (ES).

this paper presents the first release of such corpora, which includes NERC, NED, WSD and coreferencelevel annotation for these six languages.


We propose to calculate semantic similarity using distributed neural embedding models, instead of deriving it from a co-occurrence matrix directly (the latter is a socalled count-based method).

we test distributed representations of words produced by the Continuous SkipGram algorithm, first implemented in the word2vec tool (Mikolov et al., 2013).


Participants needed 14.2 minutes on average to post-edit a subtitle file segmented with the CRF-model, whilst it took them 63.7 minutes to post-edit the subtitles splitted with the counting characters method.

participants needed 49 minutes more to post-edit the same number of subtitles (50) segmented with the counting characters method.


From the gathered results, it can be observed that in general most participants found it more enjoyable to post-edit subtitles automatically segmented by the machine learning algorithm, scoring its related statements higher.

participants found it easier and less boring to post-edit, respect and use the guidelines of good segmentation with the machine learning algorithm on average.


Nevertheless, workflows emerging in the field of Digital Humanities offer the possibility to refine this process.

it has become feasible to use tools based on deep learning techniques from the field of computer vision.


When an assertion made by one comment is contradicted by i) an assertion expressed in another comment, or ii) an assertion reported in or entailed by something reported in the news article, each opposed assertion expresses a viewpoint or stance.

a viewpoint or stance can be determined when an assertion contends with another assertion in the comment stream or when a commenter is "arguing with" an assertion reported in the original article.


Our approach has many similarities to this.

we too ask subjects to answer questions about source documents under different summarisation conditions.


One example would be in cases where for historical reasons one might place a morph boundary between two potential morphs.

4 the morph pair is old enough ("ossified") to be legitimately considered as a single morph.


Each language may assimilate its borrowed neoclassical elements phonologically (but not totally) (Lüdeling, 2006).

a Greek or Latin word undergoes a minimal adaptation before being adopted by a host language.


However, using international editions poses unique challenges.

international editions use language names and grammatical terms that differ from those in the English edition.


The model extracts two important pieces of information from these instances: the strategy (cooperative vs. aggressive) of the user and an estimate of the user's preference for the options mentioned in the move.

the model knows that, when there are other good options available, a cooperative negotiator will explore those options first before insisting on their current position.


1 Moreover, both of these corpora have additional shortcomings.

the Edinburgh corpus consists only of aligned paraphrase pairs, rather than containing both paraphrase and non-paraphrase pairs; and although the MSRP corpus does include a mix of paraphrase and non-paraphrase pairs, the word alignments were annotated without taking into consideration the paraphrase status of the sentence pair.


quite challenging, since it requires discriminating among many topically related questions; by contrast, the MSRP task (Dolan et al., 2004) is to classify pairs of sentences as paraphrases or non-paraphrases, where the pairs themselves are mostly unrelated.

we found that alignments produced by Meteor (Denkowski and Lavie, 2011) were less helpful than anticipated, given that Meteor played an important role in an ensemble of MT metrics (Madnani et al., 2012) that until recently yielded the best paraphrase classification results on the MSRP corpus.


However, contrary to the MSRP guidelines-where annotators were not told whether two sentences were supposed to be in an entailment relationship-we considered it essential for the alignment decisions to be consistent with the overall decision as to whether the two questions were taken to be paraphrases in the dialogue context.

we decided that if two questions are taken to be paraphrases, they must have at least one content word aligned.


The pilot subset consists of 245 completed tests, which were sampled to represent different levels of achievement with respect to age, school year, gender and text type, as illustrated in Table  In order to make the corpus easy to interpret for scholars in the humanities, that is, to provide a format that is easy to read and understand, we use the CoNLL-X 2 shared task format instead of an XML-based representation.

since we are interested in the morpho-syntactic annotation, we have used the CoNLL-U 3 representation developed for the universal dependency annotation with some minor adaptations in order to represent corrections of student writings as spelling and grammatical errors, which occur frequently in these types of texts.


However, we can also see that about three kids deserver further study.

child 9, 10, and 20 in this graph deserve a closer look because the dark red component of the bar is significantly taller than for the other children in that same classroom.


Therefore, in order to make the language resources in personal environments publically available, to be used in language processing research, we suggest the need to seek a way to establish a consistent data management model from entering data through personal data management to storing it in public archives.

supporting personal diachronic data management may lead to fostering social synchronic data sharing.


This claim will be exemplified with the analysis of dialectal data.

the paper presents the integration of an online dialect atlas together with an online application for dialectometric analyses.


ALT is Tuscan in the sense that it is a regional atlas focusing on dialectal variation within Tuscany, a region where both Tuscan and non-Tuscan dialects are spoken.

gallo-Italian dialects are spoken in Lunigiana and in small areas of the Apennines.


6 We use a SOLR setup with only minimal changes to the schema and configuration provided in the example installation.

we add 20 fields for the domains we use for annotation.


The main assumption behind this set of features is that the salience is function of the document's structure.

these features should elucidate the way authors i) write and structure articles, and ii) distribute the important entities in the document.


In this paper we aim to provide a twofold answer to the challenges described above, and bridge the gap between the Music domain and EL.

we present ELMD, an automatically constructed corpus where named entities are classified as any of four predefined musical categories, namely SONG, ALBUM, ARTIST, and RECORD LABEL, by leveraging the hyperlinks present in a set of artist biographies.


When given data in a low-resource language on which they have not worked, how much can a small, interdisciplinary team process in a very short (24-or 48-hour) timeframe?

what NLP milestones are possible within this timeframe when conventional textual and lexical resources are unavailable?


From this, we take the nearest neighbor w Kur for each w Sor , and calculate the edits for this pair.

for example, for the pair ⟨bɾiːtanja, bɾi:tanija⟩, the edits would be ⟨b,b⟩, 10 to say, if we consider the "perfect" metric that we are seeking m * , and our naïve initial metric m0 (in this case, an unweighted Levenshtein distance), there exists some threshold constant t such that every nearest neighbor of a word w according to m * is within t distance of w according to m0.


Future work includes the extension of the corpus with additional relations and to further automatize the annotation procedure.

automatically identifying patterns that share syntactic structure would allow us to further reduce the number of pairs to be manually annotated.


Herein we plan to investigate two aspects of interlanguage in foreign students of English: the first regards their English accent and its distinctiveness and the second one is related to the particular syntactical patterns occurring in speakers that share the same native language.

we make use of the International Corpus Network of Asian Learners of English  1 to train a machine learning classifier on the speeches and transcripts corresponding to students from different Asian countries.


Although in general we did not perform well on similarity to speaker due to our choice of synthesis technique (Statistical Parametric Synthesis as opposed to Unit Selection), our performance on naturalness was good.

we had the best systems for Gujarati, Telugu, Tamil, Rajasthani and for the naturalness metric in 2014 and Hindi in 2015.


We value the data as useful for further in-depth analyses of people's interactions with devices, ambient intelligence, and robots in everyday environments.

section 2. briefly introduces the scientific aspiration for creating such an interaction corpus and gives an insight on the experimental setup as well as participant instructions.


Most importantly, all actuated items are temporally aligned with the videos and accessible as annotations.

the following events are included: • Wizard action: Points in time where the wizard triggers actions in the apartment or robot as a response to user behavior.


Acquisition, annotation and analysis of the here discussed multimodal-multisensory dataset as well as work on definition of the HRI communication model based on this specific dataset have taken place in the framework of the MOBOT project 1 , which aims at developing intelligent active mobility assistance robots for indoor environments that provide user-centred, context-adaptive and natural support.

a multimodal action recognition system able to monitor, analyze and predict user actions with a high level of accuracy and detail is currently under development.


However, none of these theories specify how the time course of local linguistic or gestural adaptation may be dependent upon the personality traits of both conversational partners.

the relationship between personality press and conversational dynamics and adaptation may be mutually influential.


We noted that the narrative texts have higher MTLDs across all scenarios, ranging between 40 and 47, as compared to ESDs with MTLDs ranging between 26 and 44 ( Figure 7).

in the narrative texts more tokens are needed to reach the set TTR of 0.71; hence, the narrative texts are more lexically diverse in comparison to the ESDs.


Concerning the mismatches between crowd(s) and experts, we can observe that the textual span of a temporal expression is the most affected dimension.

workers in both languages tend not to include premodifiers (e.g., adjectives) and determiners (e.g., articles and demonstratives) in the extent of multi-token temporal expressions.


Additional remarks concern the most frequent relations and the relationship with the average clarity score.

we observe that AFTER is by far the most preferred relation (726 annotations) followed by BEFORE and SIMULTANEOUS (579 and 517 annotations, respectively).


The first observation which emerges from this second set of experiments is that the English crowd and the Italian crowd have similar though different behaviors.

in the crowd annotation of events and temporal expressions we have observed commonalities in terms of the text span of the markables and errors.


Further explanations can also be found in the method used to annotate the expert data.

experts had to identify the pairs of elements which could stand in a temporal relations both at an intra-sentential level and at an inter-sentential level, while the crowd was presented with pre-selected pairs and only had to validate and select the best value(s).


This buttresses our assertion that differences in deception are much more subtle when other co-occurring but unrelated signals are eliminated.

when motivation, objective knowledge, and individual attributes and idiosyncrasies are controlled for, truth and lie become indistinguishable.


For the remaining pairs, we try to re-align the ones that have BLEU scores below 80% with synchronisation techniques proposed by Tiedemann (2008).

we use lexical matches between tokens of five characters or more to find the best time adjustments that maximise the ratio between non-empty and empty alignments.


As we saw in Section 2.2., aligning complex lexical units requires creating one-to-many/manyto-one as well as many-to-many links, which represent a known difficulty for WA models.

given the definition of the AER metric used, the cases that most affect evaluation are those where the complex lexical units are aligned through S-links.


Finally, regarding the very high performance of WA systems on class F[1,15]-F[1,15], we see from the fertility counts that the OOV and rare words classes contain a larger number of one-to-one links, and are thus less problematic.

this class is easier than the classes involving OOV words, since these words have been seen in the training set.


As with the DM corpus (Oepen et al., 2014), DEEPFTB is comparable in the sense that the semantic arguments of verbs and adjectives are made explicit, but it leans a little less towards a semantic representation (hence the "deep syntactic" name).

it sticks to (canonical) syntactic labels subj, obj, .


The study of conceptual dynamics over time is, beyond term annotation, another terminology-related strand of research that is relevant to the work presented here.

corpus-based approaches to the history of science have emerged recently, especially due to the growing availability of suitable text corpora.


Without the effect of rated AoA, as the CLC word frequency increased, imageability decreased.

without the effect of rated AoA, the low-frequency nouns from the CLC have higher imageability ratings than the high-frequency nouns, which is contrary to the correlation results reported at the beginning of this sub-section.


In this work, we employ statistical N-gram language models.

we use Kneser and Ney (1995)'s method, which constitutes the state of the art in word-based language modeling.


However, while more data is collected, the larger becomes the fractional amount of relevant / German vs. irrelevant / non-German data.

after downloading 300M tokens, the unfocused crawl's usable German data amounts to 61M tokens and the focused crawl's yields 277M tokens, which increases harvest by a factor of over 4.5.


2 Software available at https://github.com/DanielCoutoVale/TranslogToolset 3 Documentation: https://docs.oracle.com/javase/tutorial/uiswing/events/caretlistener.html punctuation boundaries.

4 this parser is capable of recognising Papier and ball as two different words even when they are written together as in Papierball.


As a result, both grammatical structures eines * solchen * Balls * Papier * in Table 1 and eines * solchen * Papierballs * in Table 2 could be treated by our parser as nominal groups directly composed of four words with no intermediary unit corresponding to the 1gram Papierballs * .

our parser does not need to take 1-grams as grammatical atoms from a preprocessing phase nor to build up any structure that corresponds to them in order to complete a linguistic analysis.


We test Hypothesis 2 by comparing the error categories distributions of the three Arabic texts with the previous Russian, Spanish, and Bulgarian pairs of Complex and Simplified texts.

hypothesis 2 is motivated by the fact that Arabic is an Afro-Asiatic, Semitic language (differently from Bulgarian, Spanish and Russian, which are all Indo-European), and thus should have substantial differences from these three languages.


The results also show decrease in the number of all errors for Ar37 and Ar14, except for the categories Styl, ExW, and MissW in Ar37, which is again something related to this specific text.

the much lower number of all categories of errors in Ar14 shows that Ar14 requires the least cognitive effort to be post-edited.


Because most measures were designed for IR tasks, the relevance of elements plays a crucial role in determining the evaluation score lower bound.

if all elements in the hypothesis list are irrelevant, the score should be equal to 0 or some other lower bound.


there are many elements of the same rank referred to as multiple ties.


From Section 3., we can see that conventional evaluation measures fall short from addressing evaluation criteria.

a good measure for rank-ordering problems needs to address the following: 1. multiple ties 2. non-normal rank value distribution 3. emphasis on high-rank elements In order to demonstrate our algorithm, we start with constructing an example problem.


The list L that is shown below, is ordered by rank values.

each element represents an output from rel(i), a relevance function.


We create a mapping function to assign a unique rank based on the rank subgroup.

the top-rank element is equal to the size of the element rank set, |{L}|.


: approach that aims to <entity error="tagging"> model</entity> This categorization of errors takes two different aspects into consideration: the precision of the resource (in terms of relevant/irrelevant annotations produced by the resource), and the precision of the annotation process itself (in terms of candidates that should not be linked to a resource).

we distinguished between error types that could be addressed by improving the corpus pre-processing or the annotation process, and error types coming directly from the resource.


It appears that the IR approach has a much more stable behavior on the frequencies that the system of (Ferret, 2013).

adjusted-Okapi provides relatively good results for low-frequency words.


To illustrate this last point, we report in Table 4 the performance obtained by the adjusted Okapi thesaurus when expanding queries with the first 10 neighbors of each noun, but excluding those who are listed as neighbors in WN or WN + Moby.

we only keep the neighbors judged as false positives by the intrinsic evaluation.


Because the data was intended to support machine translation system development, the translations were specified to prioritize meaning fidelity over fluency.

translators were instructed to neither add nor remove any information content from the source sentence when creating the translation.


Within the context of BOLT's overarching goal of improving machine translation capabilities in informal data genres, the BOLT IR task focused on advancing the state of the art of information retrieval over these genres (DARPA, 2011).

bOLT IR seeks to support development of systems which could: 1) take as input a natural language English query sentence, 2) return relevant responses to that query from a large corpus of informal documents in the three bOLT languages (Arabic, Chinese, and English), and 3) translate relevant responses into English where necessary (i.e.


Over the course of the BOLT program, our approach to query development and assessment changed to reflect emerging requirements as well as challenges inherent to the assessment task.

we introduced a decision points model for query assessment that allowed us to achieve improved inter-assessor consistency.


Within each language, there are differences in the correlation as a function of the words' part of speech.

the imageability values of nouns are retained much better than either adjectives or verbs.


However, in conversational texts, the scope and focus of negation may be located in the same utterance or in the previous dialogue context (i.e., inter-sentential negation), such as the previous utterance.

our focus was on annotating explicit negations in student responses recorded during actual tutorial dialogue interactions between high-school students and an intelligent tutoring system (called DeepTutor) while considering the tutorial dialogue context to interpret students' negations.


Roles are grouped by status, based on whether they are essential to the meaning of the frame.

roles are called "core" if they "instantiate a conceptually necessary component of a frame, while making the frame unique" (Ruppenhofer et al., 2006) 2 .


First, complying with the lexical orientation of FN, frames related by inheritance are assumed not to give rise to polysemy.

rEASON is annotated only on LUs inherently indicating volitional causation, either because a subcategorization frame making the agent of the action explicit exists, as with The rain forced me to go, or simply because the LU cannot describe a non-volitional CAU-SATION, as is the case of the French connective puisque (since) (Degand and Pander Maat, 2003).


In particular, REASON is annotated only on LUs inherently indicating volitional causation, either because a subcategorization frame making the agent of the action explicit exists, as with The rain forced me to go, or simply because the LU cannot describe a non-volitional CAU-SATION, as is the case of the French connective puisque (since) (Degand and Pander Maat, 2003).

a volitional cause expressed using a LU not implying volition per se, as in I left because of the rain is considered a case of CAUSATION, differently from the discourse annotation tradition (Degand and Pander Maat, 2003).


Extracting meaning from text has received considerable attention in the last decade.

semantic role labeling and efforts focused on spatial meaning-both corpora development and automatic tools-have become popular.


Therefore, we define a RE rather loosely as any substring in the utterance that contains information about an object.

we included spatial prepositions like along and through, transitive motion verbs like cross, and mentions of relative direction like to the left.


It is noteworthy that the basis of noun semantic role labeling is the verb semantic roles.

all the nouns that derive semantically or morphologically from the related verbs are labeled exactly like the verbs.


For example, in sentences such as CRF-based POS tagging has achieved state-of-the-art accuracy, we can recognize various fragments of information in addition to "CRF is used for POS tagging".

we can read that the state-of-the-art accuracy is achieved for POS tagging.


The location feature is effective for FOCUS but not for TECHNIQUE, while semantic features are more effective in finding DOMAIN and TECHNIQUE.

tECHNIQUE can be more effectively found using only semantic features by both automatic and gold annotation than using syntactic features.


We would like to study the effect of selection of documents for the training diachronic corpus.

we study the following configurations of the diachronic corpus.


Both training and decoding use one I-vector per speaker.

all frames of an individual speaker have the same fixed dimensional (100 in our system) I-vector added to the input layer of the neural network.


Among the 489 sentences which have errors in both models, 286 of them have exactly the same errors as type 1 errors, labeled by (1) in Table 7.

59.49% of all the shared errors are identical.


Hausa is part of the family of Afroasiatic languages.

hausa is the most spoken language among the Chadic languages (Vycichl, 1990).


The synthesis of sports commentaries has also aroused some interest (Krstulović et al., 2007;Picart et al., 2013;Brognaux et al., 2013).


Default Inference holds between two propositions when one proposition provides a reason to accept another proposition.

a supporting claim can be used to answer the question "why p?".


(Rahwan et al., 2007) and constitutes a part of the AIFdb database.

people with severe peanut allergies have the right to be protected.


Concerning the common assumptions that sources correspond to named entities (NEs), the corpus shows that proper nouns are only a relative majority of sources (40%) as reported in Table 3, while a considerable number of sources are expressed by common nouns (30.7%), only in part referring to an NE.


While the current encoding of attribution is rather comprehensive, some additions would be desirable.

it would be useful for the annotation to also encode the entity the source refers to.


Specific sentences contain details and can be used to support or explain the general sentences further.

general sentences create expectations in the minds of a reader who would definitely need evidence or examples from the author.


As shown in Figure 1, the distribution of the ratings is roughly normal, with mean at the slightly general side.

most sentences are a mixture of general and specific information, confirming the need for a rating scheme rather than a binary one.


The latter, described in Yang and Nenkova (2014), represents how much the text is factual and how well the content is expressed in a "direct, succinct manner".

our articles overlap with those annotated in Yang and Nenkova (2014), so we compare the content density scores of lead paragraphs annotated by Yang and Nenkova (2014) with their specificity.


Since we wanted to create a dataset for training and testing of automatic stance detection systems, we use only those instances for which inter-annotator agreement was greater than 60%.

we include only those instances for which the majority stance label is chosen by at least 60% of the annotators.


Below we illustrate some examples where the use of similarity is not always the best choice to compute relevance.

in Example 3 we notice that machines are not aware of the fact that Japan's scientific research program is called JARPA II, while in Example 4 machines do not understand that the "rescued whales" were stranded about 200 meters from the shore.


In this paper, we generate, validate and share semantic vectorial models that are built over tweets by consistently mapping in the same vectorial space both words and emojis.

we propose distinct approaches to jointly generates vectorial embeddings of both words and emojis of tweets.


Each participant was asked to rate each pair by assigning a score of similarity and a score of relatedness, both ranging from 1 to 4.

for each pair, the participant was asked if he/she agreed with the following statements (1 = totally disagree, 4 = totally agree): • I consider the two emojis equivalent (similarity) • I can imagine a situation in which I would use the two emojis together (relatedness) All the participants were familiar with Twitter language and the use of emojis.


Each scheme is meant to formulate an activation area for a target word w i either by local set operations on unimodal activation areas (from text and image-derived data) or by global algebraic operations on normalized unimodal semantic similarity scores.

the local fusion scheme selects for each target w i two unimodal neighborhoods of a small fixed size (e.g., 100 neighbors) and then performs set operations (e.g., union, intersection) on these neighborhoods to obtain a crossmodal activation area.


Both textual and visual modalities were fused in the first layer, while the textual modality was used for the computation of similarity in the second layer.

the CC feature type was employed for computing similarities computation (instead of CT) based on the very good performance of the VS/CC approach.


As an event coreference corpus, ACE 2005 has a major weakness: it adopts a strict notion of event identity.

two event mentions were annotated as coreferent if and only if "they had the same agent(s), patient(s), time, and location" (Song et al., 2015), and their event attributes (polarity, modality, genericity, and tense) are not incompatible.


Our goal is to work with this realistic version of the event coreference task and presenting one of the first results on the new KBP 2015 event coreference corpus.

we propose a multi-pass sieve approach to event coreference resolution.


More recently, Araki and Mitamura (2015) have evaluated their event coreference system on the ProcessBank corpus (Berant et al., 2014), a corpus of 200 paragraphs taken from a biology textbook.

they performed event trigger identification and event coreference resolution simultaneously using a structured perceptron.


Despite the fact that the corpus is annotated with event arguments, the version of the corpus we employ in this paper (i.e., the version available to the participants of the Event Nugget Detection and Coreference Task in the KBP 2015 Event Track) is only annotated with event mentions, event triggers, and event hoppers.

it does not contain any event argument annotations.


3 As mentioned in the introduction, since each word can trigger multiple event mentions having different types/subtypes, we train one CRF for each type.

for classifier of type t j , we create one instance for each word w i , assigning it a class label that indicates whether it begins a trigger with subtype s jk (Bs jk ), is inside a trigger with subtype s jk (I-s jk ), begins a trigger with other types (B-t m =j ), is inside a trigger with other types (I-t m =j ) or is outside a trigger (O).


We train the CRF to jointly identify and determine the semantic type of each entity mention.

we create one instance for each word w i , assigning it a class label that indicates whether it begins an entity mention of type t j (B-t j ) , is inside an entity of type t j (I-t j ) or is outside an entity (O).


Once an antecedent has been selected for em 2 , we will process the next mention in the text.

we will not select more than one antecedent for each event mention.


Machine Learning sieve: this sieve exploits the information provided by the baseline system described in the previous section.

two event mentions are posited as coreferent if their coreference probability exceeds a certain threshold according to the baseline mentionpair model.


The Transcription Bottleneck is a metaphor that is often used to describe the problem that large amounts of digital audio and video language recordings from language documentation projects are only available in the original audio or video format.

the recordings in digital language archives are often accompanied by some metainformation that includes, for example, information about authors, speakers, origin, or source of the material but, crucially, transcriptions of the audio or video material are not available or existent for most of the archived recordings, neither linguistic annotations or even translations.


Many graduate students from the Department of Linguistics at Indiana University have participated and contributed to the resources.

we are grateful to Aaron Albin, Zac Branson, Levi King, and Alec Wolyniec for their help and support in the early phases.


In both cases, the Precision score is considerably higher than the Recall.

in the real test set the overall Precision is remarkably high and it stays more or less constant for the different acoustic conditions.


Most time consuming (per word) is Text 3, interestingly so in all three translation modes.

taking the per word translation time as an indicator for translation difficulty, the graph suggests that relative difficulty remains (more or less) similar across the different   The ENJA15 study is part of a larger multilingual translation corpus in which six short English texts are translated under various different conditions into a number of different target languages, which so far also include Chinese, Danish, German, Hindi, Spanish, and now also Japanese.


Figure 11 shows that target languages closer to English have lower HTra and HCross values (below 2) than Hindi and Japanese which are very different in structure and organization from the English source language.

translator seem to have more choice as how to render the target text (conceptually and procedurally) for more remote languages, as compared to the three European languages in our corpus, which are closer in terms of language and culture.


We review why this task is impossible in general and how the texhs Haskell library uses a layered architecture and selective early evaluation (expansion) during lexing and parsing in order to provide access to structured representations of L A T E X documents at several levels.

its parsing modules generate an abstract syntax tree for L A T E X documents after expansion of all user-defined macros and lexer-level commands that serves as an ideal interface for the extraction of interlinear glossed text by texigt.


One main challenge is that IGT encodings in L A T E X documents may contain arbitrary T E X and L A T E X macros from various sources, including (a) standard macros for font styles, citations, footnotes, cross-references or even lowlevel formatting instructions, (b) macros from imported packages, like tipa 5 for phonetic symbols or leipzig 6 for standard glossing abbreviations, and (c) additional userdefined macros of arbitrary complexity, as illustrated in Figure 4.

robust extraction of IGT data from L A T E X documents is only possible on the shoulders of a full-blown T E X parser.


They can vary from physical elements found in the input text (character offsets or XPointer expressions referring to specific points or ranges within an XML document) to annotation items resulting from earlier annotation processes.

aWa allows linguistic annotations produced in a given step to be later used as anchors in a subsequent linguistic annotation phase.


For instance, a dep link addresses an unadjacent dependency, such as in the extraposition below.

speaking, an eq link represents the coreference between the head nodes of the two linked segments.


For some reference implementations there were some minor differences with the original implementation.

some feature extractors that specified a window had unintuitive behavior at sentence boundaries which we decided to change, as we could see no principled reason for the exact original behavior.


Secondly we present the main experiments of this paper using a linear SVM classifier and a larger sample of the corpus.

we use the LIBLINEAR 3 package (Fan et al., 2008) which has been shown to be efficient for large-scale text classification problems such as this (Malmasi and Dras, 2015).


The first-person form appears in a quotation (reported by a journalist, for instance) or in the speech or thoughts of a character in a literary text (memoirs and autobiographies are not subject to this problem, except where they include the utterances of other persons besides the author).

erotic or pornographic fiction was marked in this category.


And for some languages it does not make sense to use syntactic weights, as they do not reflect the role of the entity in the same way as for English.

it has been proven that the same patterns of syntactic constructions do not hold for eg.


We conducted a series of experiments to test our toolkit and to test our hypothesis that patterns of syntactic items between adjacent sentences can be better modelled through a latent alignment model.

a second aim was to check whether our IBM Model 1 formulation for the syntax model outperforms the original syntax model.


In this paper, we focus on detecting blameworthiness and praiseworthiness based on the "Path Model of Blame" presented in (Malle et al, 2014).

we propose an approach by adapting the original Path Model of Blame and combine natural language processing techniques for the detection of blame or praise expressed in text.


The interest of organizations and companies in this task has increased in recent years, due to the rise of social media.

twitter has become one of the most useful social networks for trending analysis, thanks to its abilities to capture popular trends and the easy interaction among its members.


The experimental results show how the multilingual approach, which is the only one able to consider features coming from the two languages, is the one that obtains the best performance under a number of different feature combinations.

table 4 shows the accuracy obtained on   table 5: Micro-averaged F1 (%) on the code-switching set the code-switching collection by: (1) the English monolingual model (en) , (2) the Spanish monolingual model (es) and the multilingual model (en-es).


Out of the 3,862 text units, 284 (7.4%) contain only MSA tokens, and 725 (18,8%) contain only Darija tokens.

2,853 (73,9%) text units are true code-switched instances.


For some formats also so-called roundtripping is possible.

the outcome of certain e-Services can be stored in the original format again.


The centerpiece of interlanguage resides in the concept of fossilization, i.e., "the permanent cessation of target language learning before the learner has attained the L2 norms at all levels of linguistic structure" (Selinker, 1972;Han, 2013).

the language learning process rarely (Lardiere, 2006) leads to a full, or native-like acquisition of the target language.


Corpora of original and translated language are essential for empirical investigation of theoretically-motivated hypotheses from the field of translation studies (Baker, 1996).

these corpora have been extensively used for investigation of transfer of the source language into the target one (van Halteren, 2008;Popescu, 2011;Koppel and Ordan, 2011).


In the present contribution, we discuss missing correspondences and turn our attention to the statistical inference of correspondence rules.

we use the Minimum Description Length principle (Grünwald, 2007) in order to supplement our comparative-linguistic correspondence rules.


MDL proposes that good rules are those that succinctly describe the data.

we use a model realized with a two-part code (Grünwald, 2007).


Our third dataset (UNV) represents a set of pseudoannotations.

it consists of the joint output of our two state-of-the-art metaphor detection systems.


In order to enable a finer-grained understanding of metaphoricity, we instructed the annotators to employ a four-point metaphoricity scale to allow for more nuance than a simple literal/metaphoric flag.

8 annotators were asked to rate the degree of metaphoricity between the candidate source and target terms on a scale from 0 to 3, taking into consideration the context of the entire sentence (and, if available, additional paragraph-level context).


For this purpose, they were provided with two lists -one containing 32 target domains (see Table 4) and one containing 114 source domains (see Table 5).

annotators were provided with a single CM target domain and two CM source domains produced by our conceptual metaphor mapping system (Mohler et al., 2014).


For example, there cannot be two LSFB SIGN with the same value of attribute ID Gloss.


Note that we follow a "concatenation" approach to POS, based on the model of the APPRART tag.

word tokens that are contractions of two lexical units 9 are tagged with the concatenation of the POS involved.


Our aim is to create a simple-to-use, one-binary and a single-model tool (per language), to easily process raw text to CoNLL-U-formatted tagged and/or parsed dependency trees (with morphological features if available in UD).

our goals are • state-of-the-art tools for tokenization, morphological analysis, part-of-speech tagging and dependency parsing, • free C++ tools available under the Mozilla Public License (MPL) 2.0 license (code) and CC BY-NC-SA 4.0 license (models), • simple tool with a single model (per language), easily trainable with custom data (CoNLL-U format), • trained models provided for as many UD treebanks as possible, • no feature engineering, no external morphological dictionary, no language specific knowledge, • efficient programming design in terms of RAM and disc usage.


The tagset presented here consists of 67 tags and aims to adequately represent the morphosyntactic properties of the Albanian language.

combinations of preposed articles or particles with words of other word-classes are treated in a linguistically sensible way.


To do so, we have opted to estimate the correlation between the 'confidence' and the 'probability of correctness'.

to assess the odds of the automatic tag assigned to a token being correct when the system 'word confidence' is high.


While generally absent from semantic ontologies like the Princeton Wordnet (Fellbaum, 1998), interjections are abundant in many different corpora.

any corpus that includes direct discourse is likely to contain interjections.


Our corpus is composed of 5078 sonnets written during the 16th and 17th centuries in Spanish (Castilian  (Moretti, 2007), we want a representative corpus of the kind of sonnets that were written on that period, not only the canonical sonnets.

the corpus must represent the literary context of any Golden Age poet.


number of surface forms that can refer to a meaning.

the OKE2015 and Wikinews/MEANTIME datasets stand out in their high number of maximum meanings per surface form and standard deviations.


Ashcroft is the source of the two opinions, told and was determined, and to take every conceivable action is the target of the second opinion.

there are two Is from relations: one between Ashcroft and told and the other between Ashcroft and was determined, and there is one Is about relation between was determined and to take every conceivable action.


Unlike in the pipeline approach, where entity extraction influences implicit opinion identification, which in turn influences relation extraction (but not vice versa), in a joint inference approach, all three tasks can influence each other.

recall that one problem with the pipeline approach is that if the entity extraction model fails to extract an entity, the opinion-argument classifiers cannot extract the relations for the opinion in consideration.


Given this setup, if the opinion-argument classifier is highly confident that an Is about relation exists between two candidate entities, then these two entities will likely be extracted as an opinion and a target even if the entity extraction component fails to extract them or the target-implicit classifier erroneously determines that the opinion candidate is target-implicit.

the final entity extraction decisions and relation extraction decisions will be made jointly by the entity extraction model, the argument-implicit classifiers, and the opinion-argument classifiers by considering the confidence values they individually assign to the extraction decisions.


Constraint (3) enforces the consistency between opinion-argument classifiers and the argumentimplicit classifiers.

an opinion candidate can be related to source and target arguments if it is not an argument-implicit opinion.


Constraint (4) enforces the consistency between the opinion-argument classifiers and the entity extractor.

the relations between opinion and arguments should be consistent with their entity types (i.e., a Is from relation must involve an opinion and a source, and a Is about relation must involve an opinion and a target).


Constraint (5) enforces the consistency between the opinion-implicit classifiers and the entity extractor.

an opinion candidate that is not argument-implicit must be an opinion.


Hypothesizing that some verb senses and argument roles defined in VerbNet (Schuler, 2005) are useful for identifying text spans corresponding to opinions and their arguments as well as their relationships 4 , we introduce three constraints that exploit the PropBank-style semantic role labels provided by Mate Tools (Björkelund et al., 2009).

constraint (6) states that (1) a span candidate associated with a particular verb sense should be assigned a non-none label, and (2) a span candidate associated with a verb argument should be assigned a non-none label.


We create one ILP program for each test sentence.

for each test sentence, let O be the set of opinion candidates (provided by the 30-best CRF output), A k be the set of argument candidates (also provided by the 30-best CRF output), where k denotes the relation type (Is about or Is from), and S be the union of O and A k .


First, while ILP incorporates the confidence values associated with the various classifiers' outputs into the objective function, these outputs are incorporated into an MLN as soft evidence, which can be thought of as our prior belief that a given atom (i.e., a grounded query predicate) is true.

we include as priors the atoms Span(s,l = N) with weight w e when p(s = l|x) ≥ γ, where p(s = l|x) is the probability that the CRF thinks span s has entity type l given evidence x.


This is appealing from a user's perspective: a user can focus on encoding the constraints on the outputs, and leave the grounding of these predicates and formulas entirely to the MLN inference engine.

when applying an MLN to different problem instances, the first-order formulas remain unchanged: all we need to change are the evidence predicates (i.e., the observations).


In fact, the encoding process will only become harder as a constraint involves more variables.

iLP puts a much larger burden on the user than MLNs in terms of problem encoding, especially when the problem involves encoding complex output constraints.


However, existing weight learning algorithms cannot take advantage of such prior information during the training process (Venugopal et al., 2014).

prior information can only be applied during test time.


It seems to have benefitted more from joint inference than ILP.

its better performance on extracting both types of relations is partly responsible for its superior performance on Source and Target extraction.


While MLN SRL has similarly benefitted from joint inference (its relatively strong performance on extracting Is from relations has contributed in part to its strong performance on Source extraction), it has also suffered from joint inference.

its poor performance on extracting Is about relations has contributed in part to its poor performance on Target extraction.


We adopt the Merriam Webster 3 definition of credibility that states: credibility is the quality of being believed or accepted as true, real or honest.

a credible (micro)blog is one which holds enough evidence to be believed or accepted as true, real or honest.


In Table 3   cross-lingual aspect of our data.

we provide the number of event and entity instances annotated and linked in the four languages through a unique identifier.


Our first research aim is to characterize patterns in childdirected speech and to relate them to first language acquisition theory from a cross-linguistic perspective.

we want to identify how children can acquire any language effortlessly, despite the remarkable diversity of linguistic structures in the world's 7000 or so languages.


A coefficient of stability of 1 says that each subject scores are perfectly correlated.

each subject scored the exact same thing on T1 as they did on T2.


The focus of visual attention is one such behavior and we wish to gain deeper understanding of how it is effected and explore patterns it exhibits under different interaction conditions.

we wish to gather data from multi-party interactions where a human is performing the same task as the robot would -in this case leading and taking part in simple knowledge-based games, with and without objects.


This definition has been further analysed by the Article 29 Data Protection Working Party (hereinafter: WP29) in its Opinion 4/2007, which advocates for a broad understanding of the concept.

according to WP29's analysis it covers not only 'objective' information (i.e.


It would therefore necessitate the data subject's consent, distinct from the one given for primary processing, which this time certainly cannot be implied.

in order to validly consent for such secondary processing, the user would need to be throughly informed.


In this paper, we discuss the requirements that a long lasting linguistic database should have in order to meet the needs of the linguists together with the aim of durability and sharing of data.

we discuss the generalizability of the Syntactic Atlas of Italy (ASIt), a linguistic project that builds on a long standing tradition of collecting and analyzing linguistic corpora, on a more recent project that focuses on the synchronic and diachronic analysis of the syntax of Italian and Portuguese relative clauses.


The FLaReNet Strategic Agenda highlighted the most pressing needs for research areas, such as Natural Language Processing, and presented a set of recommendations for the development and progress of LRs in Europe (Soria et al., 2014).

the recommendations are organised according to broad thematic clusters that go from availability, sharing and distribution to coverage, quality and adequacy.


we focus on the case study of the Syntactic Atlas of Italy (ASIt) that builds on a long standing tradition of collecting and analyzing linguistic corpora, which has given rise to different efforts and projects on various languages over the years.


The recordings need to be good enough so that a trained expert who speaks the language can decipher the content.

documentary linguists are not necessarily concerned with the needs of speech and natural language processing researchers or corpus linguists.


In COCOA, they are encrypted in a way that they cannot be decrypted without also downloading the WARC files.

the sources of the documents are obfuscated.


The LRE Map is mapped onto the Earth, thanks to countries, and the language resources described acquire a geographical feature.

11 http://saffron.insight-centre.org/ the LRE Map can be analyzed according to geographical dimensions.


Moreover, as the Wizard back office allows the adding and modification of legal features in a user-oriented way, the structure of the legal feature selection can easily be reviewed to include other features that may be relevant for other licenses.

it would be of interest to gather contributions from other initiatives concerned by LRs licensing like CLARIN or Linked Data.


First of all, we agreed on the types of language technologies most adapted to our goal, based upon the list provided by the MetaNet White Paper 3 (Mariani et al., 2010).

three of them caught our attention, considered as representative for the processing and further dissemination of regional languages: automatic translation, voice recognition/synthesis and spell checkers.


Beyond those main channels, other channels of information were exploited.

we would like to mention the participation of Lo Congrès 9 , the interregional organisation for the regulation of the Occitan language: they provided a significant list of resources for the Occitan language that were not identified from the main channels.


At this point, Shared is the cardinality of the intersection of the top contexts of the target words.

aPSyn, instead, is the weighted cardinality of the intersection, where the average ranking of the common features is used as a weight, as shown in the equation below: for every feature included in the intersection between the top N features of 1 , ( 1 ), and 2 , ( 2 ), aPSyn will add 1 divided by the average rank of the feature, among the top PPMI ranked features of 1 , 1 ( 1 ), and 2 , 2 ( 2 ).


A key assumption of corpus-based approaches is that similarity between words can be measured by looking at words co-occurrences.

following the Distributional Hypothesis (Harris, 1954;Firth, 1957), these methods assume that words occurring in similar contexts are also similar.


APSyn was tested on the ESL and TOEFL questions, outperforming the Vector Cosine and the co-occurrence, plus several lexicon-based and hybrid models.

our results are above those reported in the literature for non-English US college applicants on the TOEFL test (64.50%).


(Pustejovsky, 2013) introduces the notion of an object's habitat, which encodes these circumstances.

assume that, for an artifact, x, given the appropriate context C, performing the action π will result in the intended or desired resulting state, R, i.e., C → [π]R. if a context C (a set of contextual factors) is satisfied, then every time the activity of π is performed, the resulting state R will occur.


The possible values of ROTATSYM and REFLECTSYM are intended to be world-relative, not object-relative.

because we are only discussing objects when situated in a minimal embedding space (even an otherwise empty one) wherein all coordinates are given Cartesian values, the axis of rotational symmetry or plane of reflectional symmetry are those denoted in the world, not of the object.


In reality, of course, there are many more attribute categories than the four listed above, but the goal in VoxML is to use these types as the basis for an underlying cognitive classification for creating measurements from different attribute types.

these scale types are models of cognitive strategies for structuring values for conceptual attributes associated with natural language expressions involving scalar values.


Each rate means whether the first correct word was in rank of top 1 and top 5, respectively.

each is an accuracy of the first correct word in the top N. As shown on the table, the proposed method expressed higher accuracies than the baselines.


Our goal is to learn representations for medical concepts.

we would like to project a given concept y to a kdimensional vector v y ∈ R k .


The liberty of the player guarantees a large number of possible questions to ask about the game, but the game follows nonetheless a logic that can be learned by a QA system to increase its ability to understand the meaning of the questions.

our goal is to build a QA system that can translate a question asked by Non-factoid questions: What is the best way to spawn the two different types of Golem?